






"""simple docstring"""





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[int]					) ->							List[Any]:
  lowerCamelCase_       	=		SwinConfig()
  lowerCamelCase_       	=		swin_name.split('_'					)

  lowerCamelCase_       	=		name_split[1]
  lowerCamelCase_       	=		int(name_split[4]					)
  lowerCamelCase_       	=		int(name_split[3][-1]					)

  if model_size == "tiny":
    lowerCamelCase_       	=		96
    lowerCamelCase_       	=		(2, 2, 6, 2)
    lowerCamelCase_       	=		(3, 6, 12, 24)
  elif model_size == "small":
    lowerCamelCase_       	=		96
    lowerCamelCase_       	=		(2, 2, 18, 2)
    lowerCamelCase_       	=		(3, 6, 12, 24)
  elif model_size == "base":
    lowerCamelCase_       	=		128
    lowerCamelCase_       	=		(2, 2, 18, 2)
    lowerCamelCase_       	=		(4, 8, 16, 32)
  else:
    lowerCamelCase_       	=		192
    lowerCamelCase_       	=		(2, 2, 18, 2)
    lowerCamelCase_       	=		(6, 12, 24, 48)

  if "in22k" in swin_name:
    lowerCamelCase_       	=		21841
  else:
    lowerCamelCase_       	=		1000
    lowerCamelCase_       	=		'huggingface/label-files'
    lowerCamelCase_       	=		'imagenet-1k-id2label.json'
    lowerCamelCase_       	=		json.load(open(hf_hub_download(_lowerCamelCase						,							_lowerCamelCase						,							repo_type='dataset'					)						,							'r'					)					)
    lowerCamelCase_       	=		{int(_lowerCamelCase					): v for k, v in idalabel.items()}
    lowerCamelCase_       	=		idalabel
    lowerCamelCase_       	=		{v: k for k, v in idalabel.items()}

  lowerCamelCase_       	=		img_size
  lowerCamelCase_       	=		num_classes
  lowerCamelCase_       	=		embed_dim
  lowerCamelCase_       	=		depths
  lowerCamelCase_       	=		num_heads
  lowerCamelCase_       	=		window_size

  return config







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict					) ->							Dict:
  if "patch_embed.proj" in name:
    lowerCamelCase_       	=		name.replace('patch_embed.proj'						,							'embeddings.patch_embeddings.projection'					)
  if "patch_embed.norm" in name:
    lowerCamelCase_       	=		name.replace('patch_embed.norm'						,							'embeddings.norm'					)
  if "layers" in name:
    lowerCamelCase_       	=		'encoder.' + name
  if "attn.proj" in name:
    lowerCamelCase_       	=		name.replace('attn.proj'						,							'attention.output.dense'					)
  if "attn" in name:
    lowerCamelCase_       	=		name.replace('attn'						,							'attention.self'					)
  if "norm1" in name:
    lowerCamelCase_       	=		name.replace('norm1'						,							'layernorm_before'					)
  if "norm2" in name:
    lowerCamelCase_       	=		name.replace('norm2'						,							'layernorm_after'					)
  if "mlp.fc1" in name:
    lowerCamelCase_       	=		name.replace('mlp.fc1'						,							'intermediate.dense'					)
  if "mlp.fc2" in name:
    lowerCamelCase_       	=		name.replace('mlp.fc2'						,							'output.dense'					)

  if name == "norm.weight":
    lowerCamelCase_       	=		'layernorm.weight'
  if name == "norm.bias":
    lowerCamelCase_       	=		'layernorm.bias'

  if "head" in name:
    lowerCamelCase_       	=		name.replace('head'						,							'classifier'					)
  else:
    lowerCamelCase_       	=		'swin.' + name

  return name







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[int]						,							_lowerCamelCase			:						List[str]					) ->							Dict:
  for key in orig_state_dict.copy().keys():
    lowerCamelCase_       	=		orig_state_dict.pop(_lowerCamelCase					)

    if "mask" in key:
      continue
    elif "qkv" in key:
      lowerCamelCase_       	=		key.split('.'					)
      lowerCamelCase_       	=		int(key_split[1]					)
      lowerCamelCase_       	=		int(key_split[3]					)
      lowerCamelCase_       	=		model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        lowerCamelCase_       	=		val[:dim, :]
        lowerCamelCase_       	=		val[
            dim : dim * 2, :
        ]
        lowerCamelCase_       	=		val[-dim:, :]
      else:
        lowerCamelCase_       	=		val[
            :dim
        ]
        lowerCamelCase_       	=		val[
            dim : dim * 2
        ]
        lowerCamelCase_       	=		val[
            -dim:
        ]
    else:
      lowerCamelCase_       	=		val

  return orig_state_dict







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Optional[int]					) ->							Optional[int]:
  lowerCamelCase_       	=		timm.create_model(_lowerCamelCase						,							pretrained=_lowerCamelCase					)
  timm_model.eval()

  lowerCamelCase_       	=		get_swin_config(_lowerCamelCase					)
  lowerCamelCase_       	=		SwinForImageClassification(_lowerCamelCase					)
  model.eval()

  lowerCamelCase_       	=		convert_state_dict(timm_model.state_dict()						,							_lowerCamelCase					)
  model.load_state_dict(_lowerCamelCase					)

  lowerCamelCase_       	=		'http://images.cocodataset.org/val2017/000000039769.jpg'

  lowerCamelCase_       	=		AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'						,							'-'					)					)					)
  lowerCamelCase_       	=		Image.open(requests.get(_lowerCamelCase						,							stream=_lowerCamelCase					).raw					)
  lowerCamelCase_       	=		image_processor(images=_lowerCamelCase						,							return_tensors='pt'					)

  lowerCamelCase_       	=		timm_model(inputs['pixel_values']					)
  lowerCamelCase_       	=		model(**_lowerCamelCase					).logits

  assert torch.allclose(_lowerCamelCase						,							_lowerCamelCase						,							atol=1e-3					)

  print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''					)
  model.save_pretrained(_lowerCamelCase					)

  print(F'''Saving image processor to {pytorch_dump_folder_path}'''					)
  image_processor.save_pretrained(_lowerCamelCase					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							str          =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--swin_name''',
      default='''swin_tiny_patch4_window7_224''',
      type=str,
      help='''Name of the Swin timm model you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
  )

  _SCREAMING_SNAKE_CASE   :							Dict          =		parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)












"""simple docstring"""





import functools
from typing import Any







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						list[str]					) ->							bool:

  # Validation
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or len(_lowerCamelCase					) == 0:
    raise ValueError('the string should be not empty string'					)

  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					) or not all(
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and len(_lowerCamelCase					) > 0 for item in words					):
    raise ValueError('the words should be a list of non-empty strings'					)

  # Build trie
  lowerCamelCase_       	=		{}
  lowerCamelCase_       	=		'WORD_KEEPER'

  for word in words:
    lowerCamelCase_       	=		trie
    for c in word:
      if c not in trie_node:
        lowerCamelCase_       	=		{}

      lowerCamelCase_       	=		trie_node[c]

    lowerCamelCase_       	=		True

  lowerCamelCase_       	=		len(_lowerCamelCase					)

  # Dynamic programming method
  @functools.cache
  def is_breakable(_lowerCamelCase			:						int					) -> bool:
    if index == len_string:
      return True

    lowerCamelCase_       	=		trie
    for i in range(_lowerCamelCase						,							_lowerCamelCase					):
      lowerCamelCase_       	=		trie_node.get(string[i]						,							_lowerCamelCase					)

      if trie_node is None:
        return False

      if trie_node.get(_lowerCamelCase						,							_lowerCamelCase					) and is_breakable(i + 1					):
        return True

    return False

  return is_breakable(0					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




